The present invention relates in general to image sensors. More specifically, the present invention relates to image sensors with analog sample-and-hold circuit control to implement artificial neural networks (ANNs) using cross-point devices.
Programmed computers have been used to perform image analysis functions such as character recognition and image recognition. “Image recognition” refers to the ability of a computer to decipher and understand the information fed to it from an image, including, for example, still images, video, graphics, or even live video. In order to program computers to process visual data, the computer must be programmed to recognize patterns. “Machine learning” is a complex programming techniques that can be used to allow computers to learn image-related features. Machine learning computers can analyze thousands of images to find patterns, match the various patterns to each other, and output a meaningful analysis of them.
In machine learning and cognitive science, ANNs are a family of statistical learning models inspired by the biological neural networks of animals, and in particular the brain. ANNs can be used to estimate or approximate systems and functions that depend on a large number of inputs and are generally unknown. ANNs can include DNNs, convolutional neural networks (CNNs), and other types of neural networks. Crossbar arrays are high density, low cost circuit architectures used to form a variety of electronic circuits and devices, including ANN architectures, neuromorphic microchips and ultra-high density nonvolatile memory. A basic crossbar array configuration includes a set of conductive row wires and a set of conductive column wires formed to intersect the set of conductive row wires. The intersections between the two sets of wires are separated by so-called cross-point devices.